However we lack a subcategorisation lexicon that provides information about how many arguments a preposition like "between" takes.

we scan conjoined prepositional phrases, aggregate the objects, and attach them to the governors of the prepositional phrases.


The question remains whether training a POS tagger that takes inter-annotator agreement scores into consideration is also effective on downstream tasks.

we evaluate our best model, the CM-weighted learner, in two downstream tasks: shallow parsing-also known as chunking-and named entity recognition (NER).


Averaging destroys order information that may be needed for NLP sequence classification tasks.

we also investigate a sequencepreserving method, k-max sequence:  where [a j |P (a j )] denotes the subsequence of sequence A = [a 1 , .


5 These results suggest that it is best to optimize the attention mechanism directly on the input, so that information can be extracted  For focus on input (F=W), external attention (13) is significantly better than internal attention (11) for CNNs.

by designing an architectural element -external attention -that makes it easier to identify hedge cue properties of words, the learning problem is apparently made easier.


3 The Dataset To best of our knowledge, there is no labeled dataset that encompasses the activities on the Darknet web pages.

we have created the first publicly available Darknet dataset and we called it Darknet Usage Text Addresses (DUTA) dataset.


Moreover, when a sample contains relevant images but an irrelevant text or without any textual information, we excluded it from the dataset.

we had 5,635 samples distributed over nine classes i.e.


This auxiliary task aids the prediction of the main task (POS) in about half the languages, and improves the prediction of out of vocabulary words.

it is compelling to assess the possible contribution of FREQBIN for other tasks, as it can be easily calculated from the same training data as the main task, and requires no external resources or annotation.


Auxiliary task level distribution also plays a role, as we will discuss in Section 4.3, FREQBIN-UNIFORM consistently outperforms the skewed measure with base 5 and 10.

we have also measured the effect of using different sources of POS auxiliary data to give account for the possible differences in label inventory and corpus for all tasks, high and lowlevel, cf.


Moreover, discretized frequency distributions allow us to interpret them in terms of entropy.


(2015)) that abstracts from individual attributes.

after fitting the compositionality function, our model bears the potential of being applied to various application scenarios (e.g., aspect-based sentiment analysis) involving diverse attribute inventories.


The present approach and the work by Hartung and Frank may be considered as pairs of opposites in two respects: First, our model is based on pretrained CBOW word embeddings for representing adjective and noun meaning.

we do not encode any attribute-specific lexical information explicitly at the level of word representation.


Natural language refers to ontological attributes in terms of attribute nouns such as color, size or shape (Guarino, 1992;Löbner, 2013).

despite remaining mostly implicit in adjectivenoun phrases (cf.


In this work, we aim at a compositional approach to attribute meaning in adjective-noun phrases.

of the above assumption, our model represents adjectives, nouns and attributes as vec-tors a, n and attr , respectively, in one and the same embedding space S ⊆ R d .


We consider y as a hypernym of x if hypernymy holds in some of the words' senses.

when a pair is assigned both hypernymy and another relation, we only keep it as hypernymy.


For consistency with EVALution, we switched those pairs in BLESS, placing the meronym in the x slot and the holonym in the y slot.

after the switching, holonyms in BLESS are usually rather specific words (e.g., there are no holonyms like animal and vehicle, as these words were originally in the y slot).


However, a deeper analysis of their performance demonstrated that, as previously suggested, these methods do not capture the relation between x and y, but rather indicate the "prior probability" of either word to be a hyponym or a hypernym.

supervised methods are sensitive to the distribution of examples in a particular dataset, making them less reliable for real-world applications.


Here the sim(t, w) is the relatedness of the cluster word w to the target word t, and the f req(w, h) is the frequency of the hypernymy relation (w, h) as extracted via patterns.

a high-ranked hypernym h has high relevance, but also is confirmed by several cluster words.


Note that SenseGram outperforms all other systems according to the Fuzzy B-Cubed metric, which is maximized in the "All instances, One sense" settings.

this result may be due to Figure 3: Impact of word sense inventory granularity on WSD performance: the TWSI dataset.


Besides, our system performs comparably to the top unsupervised systems participated in the competition: It is on par with the top SemEval submissions (AI-KU and UoS) and the another system based on embeddings (AdaGram), in terms of four out of five metrics (Jaccard Index, Tau, Fuzzy B-Cubed, Fuzzy NMI).

we conclude that our system yields comparable results to the state-of-the-art unsupervised systems.


As there are almost 10 million students take the examination every year, Gaokao needs to be difficult enough to distinguish the excellent students.

it includes various types of questions such as multiple-choice questions, short-answer † Both of the two authors contributed equally to this paper.


Effectively, the difference between Path-RNN and Single-Model is that Single-Model does multitask learning, since it shares parameters for different target relation types.

we expect it to outperform Path-RNN on this small dataset, since this multitask learning provides additional regularization.


Traditional methods rely on both self-reports and impressions formed during short sessions with a clinical expert, but it is often unclear when suicide is a risk in particular.

2 conditions leading to preventable suicides are often not adequately addressed.


(iii) Most importantly, the reduction of V r again yields consistent rankings with all metrics, which are also fairly consistent with the rankings obtained in the ten times larger 100K search space.

in all further experiments we use the USF vocabulary as our search space.


In summary, the comparison of systems based on overall performance scores only lets us glimpse the proverbial tip of the iceberg.

our approach to comparative evaluation features three main points of interest: 1.


2 Although the form is often preserved, MT outputs may sound "strange" or "different" in comparison to HT ones due to the loss of meaning.

human translators generally enrich the text with the appropriate tone, style and sentiments during translation.


Results of our first experiment indicated strong correlations between quality scores of the features and human rankings.

we also investigate whether we can predict human rankings of MT translated text by combining these features since they capture different aspects of translation.


Moreover, our experiments indicate that the meaning related features can boost the performance of BLEU, METEOR and DPMFComb metrics without even specific optimization.

our method of integrating meaning related features to MT systems with ranking components can also improve the performances of other metrics instead of only relying on form based features.


Our rules do not make nouns eligible heads for verbs.

the system cannot infer relative clauses.


Assuming we have access to a scoring function Score(•, •) that tells how well a dependency tree fits the syntactic structure of a sentence, the goal of dependency parsing is to find the treeŷ such that: The size of T x grows exponentially with the length of x, |T x | = |x| |x|−2 , making an exhaustive search for the best tree impractical in most cases.

in practice, some simplifying assumptions are made.


We explore this aspect in our evaluation.

our approach relies on generic sentiment analysis, rather than on topic or domainspecific features.


When considering additional anchor pairs beyond the top-ranked pair (a c , a t ), we multiply the above contrast score by sgn(r(b c , b t )) for each such additional pair (b c , b t ).

these pairs may affect the sign of the contrast score but not its magnitude.


However, the main difficulties in applying these tests are: (i) time required, since it is a manual task; and (ii) the subjectivity of the clinician.

automatic analysis of discourse production is seen as a promising solution for MCI diagnosis, because its early detection ensures a greater chance of success in addressing potentially reversible factors (Muangpaisan et al., 2012).


However, our approach can be regarded as a more general approach to the problem of domains as it is based on automatically determining the genres present in the dataset.


In this question, we examine whether the performance of POS tagging improves if we create experts based on the topics detected by the topic modeler.

we use the topics created for the previous sections and train a POS tagging expert on the training part of each topic.


We simulate weighting training sentences by adding multiple copies to the training files of the experts.

for 2-topic experiments, a sentence with 80% probability for topic 1 will be included 8 times in the expert for topic 1 and 2 times in the expert for topic 2, rounding up small percentages so that every sentence will be added to every expert at least once.


Thus, for 2-topic experiments, a sentence with 80% probability for topic 1 will be included 8 times in the expert for topic 1 and 2 times in the expert for topic 2, rounding up small percentages so that every sentence will be added to every expert at least once.

we use a more fine grained topic model while mitigating data sparseness, but we risk adding non-typical / irrelevant sentences to experts.


Moreover, the manually corrected gold standard corpus provides the opportunity for empirical evaluations like assessing the converter and comparing dependency parsers employing the original and the universal morphological representations.

we evaluated the quality of the automatic conversion, which reveals that converting to universal dependencies is not necessarily trivial, at least for Hungarian.


In other words, the form of the verb changes when the definiteness of the object also changes (Törkenczy, 2005 In this way, the feature Definiteness needs to be applied to verbs in Hungarian, moreover, it has a language-specific feature due to the special form triggered by the second person objects.

definiteness has three possible values in Hungarian: definite, Indefinite, 2.


They may be adpositions or adverbs by origin, therefore should be tagged ADP or ADV, respectively.

we manually compiled a list that contained the original part-ofspeech of words that were tagged as verbal prefixes, for instance, el "away" was treated as an adverb and agyon brain-SUP as a noun -the latter is usually used in phrases like agyonüt "kill someone by hitting on his head".


In Hungarian, nouns that bear the suffix -nAk can fulfill several grammatical roles in the sentence such as:  While these forms do not show any difference at the morphological level, they have very different roles at the syntactic and semantic levels.

we decided not to make any distinction in the morphological annotation but they should have different syntactic labels.


As for the first, it should always be attached to the possessed noun, whereas the second one is attached to a verb (see also Examples 18 and 19 with the dative morphological case).

the parser can learn these finegrained distinctions, which might be beneficial for the unlabeled attachment scores as well.


The bracketing structure of AMR is hard to maintain because the prediction of relation (with left parenthesis) and the prediction of an isolated right parenthesis are not correlated.

the output AMR sequences usually have parentheses that do not match.


Also, if we set n too high, although the output vocabulary size becomes smaller, some of the frequent output labels that the model handles well originally will be put into the coarse-grained types, losing information in the recovery process.

we can see from the plot that after the optimal point the Smatch score will drop.


Thus we can see from the plot that after the optimal point the Smatch score will drop.

we choose to set n = 50 in the subsequent experiments.


Since both oracle and extractive summaries are sets of sentences, it is easy to check whether a system summary contains sentences in the oracle summary.

f-measures, which are available to evaluate a system summary, are useful for evaluating classification-based extractive summarization (Mani and Bloedorn, 1998;Osborne, 2002;Hirao et al., 2002).


Since ROUGE n evaluation does not identify which sentence is important, an Fmeasure conveys useful information in terms of "important sentence extraction."

combining ROUGE n and an F-measure allows us to scrutinize the failure analysis of systems.


The computational complexity of naive exhaustive methods is exponential to the size of the sentence set.

it may be possible to apply them to single document summarization tasks involving a dozen sentences, but it is infeasible to apply them to multiple document summarization tasks that involve several hundred sentences.


Although GA or greedy algorithms are widely used to solve NP-hard combinatorial optimization problems, the solutions are not always optimal.

the summary does not always have a maximum ROUGE n score for the set of reference summaries.


argued (Takamura and Okumura, 2009), the summaries optimized with non-content words failed to consider the actual quality.

we excluded stopwords for computing the ROUGE 1 scores.


The results demonstrate that the oracle summaries are much less similar to the greedy summaries at the sentence-level.

it might not be appropriate to use greedy summaries as training data for learning-based extractive summarization systems.


Since both the oracle and system summaries are sets of sentences, it is easy to check whether each sentence in the system summary is contained in one of the oracle summaries.

we can exploit the F-measures, which are useful for evaluating classification-based extractive summarization (Mani and Bloedorn, 1998;Osborne, 2002;Hirao et al., 2002).


Moreover the fixed-size memory in NTM has no memory allocation or de-allocation protocol.

unless the controller is intelligent enough to track the previous read/write information, which is hard for an RNN when processing long sequences, the memory content is overlapped and information is over-written throughout different time scales.


However, finding the highest-scoring label sequence is a computationally hard problem, since the conversation-level RNN does not lend itself to dynamic programming.

we employ a greedy decoding approach, where, going left-toright, at each step we choose the y t with the highest probability in the local DA distribution.


To answer this question, we create a variant, denoted woUttRNN, where attentional coefficients are applied directly to the token embeddings.

equation 4 is changed to equation 9: As seen in Tables 4 and 5, removing the utterance-level RNN (woUttRNN) reduces the accuracy and increases the negative log likelihood for the training, development and test partitions of both datasets.


The LSTM basis function is calculated with a fixed vector g g g 0 instead of the previous time step's vector.

equation 7 becomes equation 12: As seen in Tables 4 and 5, HA-RNN outperforms woConvRNN on the training/development/test partitions of both datasets.


An-other advantage of this approach is that we can then separately analyze and improve the performance of the two stages.

we evaluate both subtasks to give a more complete picture of when the system fails and succeeds.


The frameId step reduces the hundreds of role labels in FrameNet to a manageable set of up to 30 roles.

frameNet SRL differs from PropBank SRL (Carreras and Màrquez, 2005), that only uses a small set of 26 syntactically motivated role labels and puts less weight on the predicate sense.


Indeed, if two templates are equivalent, we can replace one with the other and still generate the correct equations.

after getting human judgements on alignments, we reconcile all the templates using TEMPLEQUIV as the final step of annotation.


Let probability of getting the same solution even when the template are not equivalent be (T 1 , T 2 , γ) < 1.

the probability that solution is same for R rounds for t 1 , t 2 which are not equivalent is ≤ R , which can be made arbitrarily small by choosing large R. with a large enough R, obtaining Γ = ∅ from tEMPLEQUIV implies there is a γ under which templates generate equations with the same solution, and by definition, are equivalent.


We use an LSTM-BLSTM of the same size for all languages, although the amount of training data varies by roughly two orders of magnitude.

it is a valid question if a larger model specifically designed for Czech/UD or a smaller model for Turkish/UD would improve the results.


Indeed, we found that for one of the languages we investigated, for Hungarian, singlesource MRI works at least as well as multi-source MRI-presumably because its paradigms almost exclusively contain SINGLEFORM configurations.

single-source MRI is probably preferable for Hungarain since single-source is simpler than multi-source.


6 In contrast, dependency trees do not have nodes with multiple parents.

when creating a new arc, transition systems for dependency parsing check that the dependent does not already have a head node, preventing the node from having additional parents.


Each token in x is mapped to a constant number of nodes in the graph by Shift.


However, it is difficult to learn a policy over this large a state space in a reasonable number of dialogs.

we learn a dialog policy over a summary state as in previous work Gašić and Young, 2014).


Its parser and policy were updated incrementally after each training batch.

for the next training batch, the changes due to the improvement in the parser from the previous batch could, in theory, be demonstrated in the dialogs and hence contribute towards updating the policy in a manner consistent with it.


In Figure 1, the names "Pierre" and "Vinken" are aligned to their word forms on the dependency parse leaves.

we design a straightforward rule-based probability, P rule , which catches the appearance of the surface form.


In practice, the number of case frames is always larger than the number of sense in PropBank for each verb.

a mapping process that aligns case frame id(s) to PropBank verb sense is applied.


Deep case frames are compiled using automatic semantic roles that use semantic-level representation.

we consider that using deep case frames as additional knowledge has a more direct impact on the performance on SRL.


(2014), Yaghoobzadeh and Schütze (2015)): entities with similar meanings tend to have similar contexts.

we can learn a d dimensional embedding v(e) of entity e from a corpus in which all mentions of the entity have been replaced by a special identifier.


This suggests that using order aware embedding models is important for entities.


We define the word-level representation (WLR) as the average of the embeddings of the words that the entity name is the embedding of the i th word of an entity name of length n. We opt for simple averaging since entity names often consist of a small number of words with clear semantics.

averaging is a promising way of combining the information that each word contributes.


For these entities, their name can be a complementary source of information and character/word level representations can increase typing accuracy.

we introduce joint models that use combinations of the three levels.


CLR -produced by one of the character-level networks described above -is initialized randomly and then tuned during training.

it can focus on complementary information related to the task that is not already present in other levels.


FIGMENT ignores that FIGER is a proper hierarchy of types; e.g., while HOSPITAL is a subtype of BUILDING according to FIGER, there are entities in FIGMENT that are hospitals, but not buildings.

4 we modified the FIGMENT dataset by adding for each assigned type (e.g., HOSPITAL) its parents (e.g., BUILDING).


The reason is that in our setup, word embeddings are induced on the entire corpus using an unsupervised algorithm.

even for many words that did not occur in train, SWLR has access to informative representations of words.


JoBimText models provide sense distinctions that are only partially disambiguated: the list of similar and hypernyms terms of each sense, in fact, does not carry sense information.

a semantic closure procedure is applied in order to obtain a PCZ and arrive at sense representation in which all terms get assigned a unique, best-fitting sense identifier (see  for details).


when  Table 1: Excerpt of a proto-conceptualization (PCZ) for the words "bridge:NN" and "link:NN".


Intuitively, most of the benefits from our method derive from the "gold standard" information of the companion KB, and its linking to the NKG, which act as a source of supervision.

we address the research question of how much (pseudo-)supervision our method needs in terms of KB links, and whether it can be used to improve the state-of-the-art on the task of taxonomy induction.


The reviews are used to express opinions for different aspects of products, and have a wide variety of writing styles and different polarity strengths.

much previous work has focused on how opinions are expressed in review data.


However, it does not give an indication of wellformedness or readability.

we complement BLEU with a human preference evaluation.


X 13 is sibling to A through A's parent X 23 .

but X 23 is in turn neither descendant of D nor sibling of D. X 13 is not a valid inflection point for C. Now, X 12 is sibling of A through A's parent X 22 .


The time complexity of the Eisner algorithm is O(N 3 ), while checking if a tree is projective can be done reasonably faster, with a O(N log N ) algorithm.

we only apply the Eisner algorithm to the non-projective output of our greedy inference strategy.


In the majority of cases DENSE outputs trees (ranging from 87.0% to 96.7%) and a significant proportion of them are projective (ranging from 65.5% to 86.6%).

only a small proportion of outputs (14.0% on average) need to be post-processed with the Eisner or Chu-Liu-Edmonds algorithm.


No combination of parameter values instantiates U 1 G 2 (for either the depicted or other possible values of m and d).

both the English and German behavioral patterns are quite generally predicted by the model.


Now m∈{0,1} i−1 p N (m)m I is the total probability that all of a set of indices I survives erasure.

informally: That is, the cost of a word is its inherent cost minus its interaction informations with context, which are weighted by the probability that all elements of those interactions survive erasure.


Under progressive erasure noise, the probability that a subset of variables is erased increases the farther left those variables are in the context.

equation 7 expresses information locality: context elements which are predictive of w i will only get to mitigate the cost of processing w i if they are close to it.


Facebook (FB) is the most popular social networking site in the world, with 1.65 billion monthly active users during the first quarter of 2016.

6 Facebook was a natural first choice for corpus collection, but since the privacy policy of Facebook is very stringent, accessing Facebook data is challenging.


2014, who encode the bag-of-words representations of parallel sentences into the same vector.

each word is not matched directly to another word, but rather used to create the sentence's language-independent representation.


Since I(w, i) is an indicator function of whether the word w appeared in sentence i, it stands to reason that the product I(w s , i) • I(w t , i) is an indicator of whether both w s and w t appeared in i. Ergo, the numerator of Equation 7is exactly the number of aligned sentences in which both w s and w t occurred: S(w s , w t ).

: S(ws, * )•S( * ,wt) = Dice(ws,wt) 2 8This theoretical result implies that the crosslingual similarity function derived from embeddings based on sentence IDs is essentially a generalization of the Dice coefficient.


A large body of work on word embeddings demonstrates that a generic text representation, trained in an unsupervised fashion on large corpora, is useful.

we take the view that group (i) models, end-to-end learning without any representation learning, is not a good general approach for NLP.


In many languages, e.g., in German, syncretism, a particular type of systematic morphological ambiguity, is pervasive.

there is no simple morphological processing method that would produce a representation in which all inflected forms of "to ring" are marked as having a common lemma; and no such method in which an unseen form like "aromatizing" is reliably analyzed as a form of "aromatize" whereas an unseen form like "antitrafficking" is reliably analyzed as the compound "anti+trafficking".


Nonsymbolic representation learning does not preprocess the training corpus by means of tokenization and considers many ngrams that would be ignored in tokenized approaches because they span token boundaries.

the number of ngrams that occur in a corpus is an order of magnitude larger for tokenization-free approaches than for tokenization-based approaches.


mance for F-RANDOM (F 1 of .582) is better than tokenization-based W2V and FTX performance.

our proposed method seems to be an effective tokenization-free alternative to tokenizationbased embedding learning.


As K goes to infinity, we cannot represent the infinite number of θ e , ψ e , ω e and φ e explicitly.

we perform Gibbs sampling for only those parameters that are currently associated with some observations.


Given a sequence of words p = (w 1 , w 2 , ..., w d ) with window size d, the goal of the model is to discriminate the sequence of words p (the correct phrase) from a random sequence of words p r .

the objective function of the model is to minimize the ranking loss with respect to parameters θ: , where p is the set of all possible text sequences with d words coming from the corpus U , R is the dictionary of words, p r denotes the window of words obtained by replacing the central word of p by the word r and f θ (p) is the score of p. The dataset for learning the language model can be constructed by considering all the word sequences in the corpus.


If an event happened in someplace like "A bomb attack was happened in London", the location is the most important.

we first select the top 3 non-location named entities ranked by the probability θ e .


In turn, we have that : The output y C is guaranteed to contain at least one label A and one label B. t * A in Equation 1 will contain only one token while variables A and B, in reality, often span multiple tokens.

as an additional post-processing step, we run the Viterbi algorithm once more using the identified labels A, B and P as constraints.


are trained on parallel corpora, MultiCluster and MultiCCA use monolingual corpora and bilingual lexicons which are similar to our proposed methods.

for a strict comparison 8 , we train our best model (Joint + Mapping) using the same monolingual data and set of bilingual lexicons on the same 12 languages with MultiCluster and Mul-tiCCA.


Such representation schemes are, however, not an all-in-one solution for the many NLP application scenarios.

different representation methods were developed, each one focusing in a limited set of concerns, e.g., semantic relatedness measurement (Mikolov et al., 2013;Pennington et al., 2014) and grammatical dependencies (Levy and Goldberg, 2014).


This concept stems from the notion that words are always used in a context, and it is the context that defines their meaning.

the meaning of a term is concealed, i.e.


Our WSD algorithm is also unsupervised, but it requires knowledge in the form of WordNet (Miller, 1995;Fellbaum, 1998) synsets and relations as well.

our algorithm can be regarded as a hybrid approach.


As we select a single context window at every possible location in a text, our algorithm becomes deterministic, obtaining the same global configuration for a given set of parameters and input document.

our algorithm is not affected by random chance, unlike stochastic algorithms such as Ant Colony Optimization (Lafourcade and Guinand, 2010;Schwab et al., 2012;Schwab et al., 2013a).


Banerjee and Pedersen (2002) note that this is a significant limitation because dictionary glosses tend to be fairly short and they fail to provide sufficient information to make fine grained distinctions required for WSD.

banerjee and Pedersen (2003) introduce a measure that takes as input two WordNet synsets and returns a numeric value that quantifies their degree of semantic relatedness by taking into consideration the glosses of related WordNet synsets as well.


For each word in the disambiguation vocabulary that represents a synset, we compute its word embedding vector.

we obtain a cluster of word embedding vectors for each given synset.


We can naturally assume that some of the words in the cluster may actually be outliers.

we believe that using the (geometric) median instead of the mean is more appropriate, as the mean is largely influenced by outliers.


Corpora used for the evaluation of WSD methods usually contain this kind of annotations, but the MCS baseline will not work outside the annotated data.

we consider important even slightly outperforming the MCS baseline.


We consider the ShotgunWSD variant based on sense embeddings without changing its parameters, and we remove the assembly phase completely.

the algorithm will no longer produce configurations of length greater than 8, as the parameter n is set to 8.


This decision can be directly executed, or it can be combined in a more nuanced fashion with the MT system.

to modify translations using this decision, we propose and test three approaches for using in in an MT system: Post-editing: directly edit the translations T 1 or T 2 depending on the classifier's decision.


While ModelBlocks achieves competitive accuracies, it is much slower than other state-of-theart parsers available today.

we wanted to provide a point of comparison regarding the relative utility of these parsers: grouping features by parser allows us to assess the trade-off between model accuracy and the time necessary for feature extraction.


For character features, the product title is first normalized as discussed above.

character 2, 3, and 4-grams are extracted with their counts, where extractions include single spaces appearing at the end of word boundaries.


To this end, we remove listings corresponding to such "out-of-category" merchants from all top-level categories.

by manually inspecting K × 6 most probable words from the K=100 topics and J × 100 listings, where J << K, instead of 3.4 million, a few annotators accomplished in hours what would have taken hundreds of annotators several months according to the estimates in Sun et al.


Although their approach is capable of finding undisputed arguments among a given set of arguments, it does not answer why a specific argument is of inferior quality than another argument.

their approach is of limited use for guiding students since it does not pinpoint particular weaknesses of arguments.


The chance-corrected agreement scores of κ = .7672 and α = .7673 show substantial agreement between the annotators which allows "tentative conclusions" (Krippendorff, 1980).

we conclude that human annotators can reliably identify insufficiently supported arguments in argumentative essays.


We also found that annotators do not agree on arguments including terms such as "some", "various", or "large number".

extending the annotation guideline with an explanation of how to handle modal verbs, the number of premises and undetermined qualifiers could further improve the agreement between the annotators in future annotation studies.


In contrast, the CNN model is more balanced with respect to precision and recall and considerably outperforms the recall of the SVM model.

the CNN model outperforms the SVM model in finding insufficiently supported arguments in argumentative essay and performs better for recognizing arguments that are really in need of revision.


Although the convolutional neural network achieves promising results, the sufficiency criterion is only one of three criteria that a logically good argument needs to fulfill.

our approach is not yet able to separate logically good from illogical arguments.


Clitics, such as conjunctions + w+, prepositions + b+, the definite article Al+ 'the' (all of which attach at the beginning), and possessive pronouns and object pronouns + +h + +hA 'his/her' or 'him/her' (which attach at the end) can all function as individual words.

they can be represented as separate tokens in the CRF.


It should be noted that this model was trained on a collection of texts from different resources such as Spanish Wikipedia, WikiSource and Wikibooks, and none of them contains tweets.

it is possible that the main characteristics of the social media texts (such as informal style language, grammatical errors and spelling mistakes, slang and vulgar vocabulary, abbreviations, etc) are not correctly represented in this model.


Due to the tremendous increase in web content, many organizations became increasingly interested in analyzing this big data in order to monitor the public opinion and assist decision making.

sentiment analysis attracted the interest of many researchers.


However, their values are found almost similar to the classification accuracies.

only classification accuracies are reported.


This causes circular dependencies to arise when combining probabilities from multiple time steps.

pseudo-likelihoods are utilized rather than true likelihoods which is not perfect from the mathematical point of view.


To evaluate the sentiment of some test review, we calculate the perplexity of each model p + and p − with respect to the whole review.

given a probability distribution p, and a review text S composed of K sentences S = s 1 , ..., s K , each sentence s k : 1 ≤ k ≤ K is composed of a sequence of M k words s k = w k 1 , w k 2 , ..., w k M k ; we calculate the perplexity P P p (S) of a model p with respect to text S. It is a very common measure-ment of how well a probability distribution predicts a sample.


Although the output of the BLSTM network at a given time step is dependent on the whole input sequence, it is widely known that every output is more affected by the inputs at closer time steps in both directions.

a sentence-level sentiment can be deduced by comparing the average probability mass assigned to the positive class over all time steps with the average probability mass assigned to the negative class.


On the other hand, supervised models can achieve better performances than patterns on manually labeled datasets, but it is often difficult to obtain large number of annotations for the relation extraction task, and the trained models are also limited to specified domains.

we still need an algorithm to better combine the power of syntactic and lexical patterns, while reduce manual annotations.


Another example is that some true expressions could be ignored either due to errors from POS taggers and syntactic parsers.

to enlarge the coverage of opinion relation extraction, we need some method to better detect opinion expressions.


Although the relations are not completely correct, they are almost free to collect and easily amount to a large set.

we can take the relations from the pattern matching as the distant supervision, and hope the broad coverage could overcome the noise.


First, if transition matrices are stored as dense matrices, then the forward algorithm becomes identical to forward propagation through a rudimentary recurrent neural network.

a neural network toolkit could be used to carry out this computation on a GPU.


tion system, which is possible on the order of milliseconds.

our objective is finding an optimal policy that generates decision sequences with a good trade-off between higher quality Q(Y ) and lower delay D(A).


The only exception is translation into Finnish, in which perplexity on the PBMT output is slightly lower, probably because its fluency was improved by reranking it with a neural LM similar to the one 7 http://data.statmt.org/ wmt16/translation-task/ training-monolingual-news-crawl.tgz  .

our experiment shows that the outputs produced by NMT systems are, in general, more fluent than those produced by PBMT systems.


On the word level, for instance, it is obvious that the classifier needs to combine the embeddings of the source and target word to make an informed decision and not merely calculate a weighted sum of them.

we opt for an architecture with hidden layers instead (see Fig.


Unless stated otherwise, we use two hidden layers, while in Section 5.3 we further analyze the influence of parameter H. To identify which word pairs are translations, one could enumerate all translation pairs and feed them to the classifier g. The time complexity of this brute-force approach is O(|V S | × |V T |) times the complexity of g. For large vocabularies this can be a prohibitively expensive procedure.

we have resorted to a heuristic which uses a noisy classifier: it generates 2N c << |V T | translation candidates for each source language word w S as follows.


When repeated news items are identified, the number and variety of sources covering the story is an indicator of that story's relative importance.

from the end-user's perspective, we have at least two motivations for grouping different news that describe the same story: reducing redundancy and ranking stories by importance.


Although in this paper we deal only with business news, we consider redundancy and the tendency to repeat the more important events to be general properties of news streams.

we believe that our task and the proposed solution generalize to many other news-monitoring settings.


If the same entities engage in two different activities, we consider that as two distinct stories.

we manually annotated a sample of our corpus, which is more suitable for evaluating our methods.


We compute salience as a combination of prominence and frequency of an entity in a document.

prominence captures the importance of the first mention of entity e in document d: where ns(d) is the total number of sentences in the document, fs(e, d) is the number of the sentence (starting at zero) of the first mention of e in d. the prominence of entities mentioned in the title is 1.


For NEs, we use raw counts, TF-IDF and salience.

we experiment with six vector representations.


Information needs of people aim more and more at arguments in favor of or against a given controversial topic (Cabrio and Villata, 2012b).

future information systems, above all search engines, are expected to deliver pros and cons in response to respective queries (Rinott et al., 2015).


It includes relationrich corpora, e.g., AraucariaDB (Reed and Rowe, 2004), as well as much annotated web text, e.g., from (Walker et al., 2012) and (Wacholder et al., 2014).

it serves as a suitable basis for constructing an argument graph.


The right summand ensures that the total contribution of conclusion usages is normalized with the total number of premises.

the sum of all adapted PageRank scores is also 1.


Similarity: Since reflective listening includes repetition and rephrasing, we can expect to observe linguistic similarity between client and counselor speech.

we measure the degree to which the counselor matches the client language by using Linguistic Style Matching (LSM) (Gonzales et al., 2009), a technique that allows to quantify the extent to which one person uses comparable types of words to another person.


In this study, we adopt the latter approach.

for each of the above type of features there are two parameters: the order (length) of n-grams (n) and the frequency threshold (f t ).


As can be seen, in CCAT-10 there are multiple words that are author-specific and tend to appear in multiple documents by that author (both tf and df are high).

these words are useful indicators of authorship for that specific corpus.


we update the particle position value of 0 or 1 on the basis of the value of velocity.


Traditional extraction approaches require annotated examples, which makes it difficult to scale to the explosion of extraction demands.

there has been increasing interest in indirect supervision (Banko et al., 2007;Poon and Domingos, 2009;, with distant supervision (Craven et al., 1998;Mintz et al., 2009) emerging as a particularly promising paradigm for augmenting existing knowledge bases from unlabeled text Parikh et al., 2015).


These results support the hypothesis that dependency edges are usually more meaningful for relation extraction than word adjacency.

if adjacency edges get the same weights, they might cause some dependency sub-paths drop out of the top K paths, thus degrading performance.


(2010), we do not incorporate binary entity type values, but probabilistic outputs.

we allow the relation extraction system to compensate for errors of entity typing.


Yaghoobzadeh and Schütze (2015) applied a multi-layer perceptron (MLP) architecture to create context representations.

we use an MLP baseline to compute the context representation φ(c).


Probabilistic representations preserve this information.

using probabilistic representations, the RE system can compensate for noise in ET predictions.


Similarly, the curve for the model with EntEmb as entity typing component has more precision variations than the curves for the other models which use context features for ET.

joint training does not help per se but it is important which models are trained together.


The cosine similarity between icon and deity was 0.266, which is smaller than expected.

we randomly sampled 100 lines containing icon from English wikipedia text, which we used for calculating word embeddings, and found that the dominant sense of icon in wikipedia is not a religious painting or figure, but a representative person or thing' as in the wikipedia page of a football superstar David Beckham 14 : Beckham became known as a fashion icon, and together with Victoria, the couple became • • • the reason of icon's anomalous behavior is that the distribution over senses in wikipedia was a lot different from the expected one.


Unlike many other supervised or semi-supervised clustering tasks, the set of cluster labels to be inferred is disjoint from the gold standard labels.

we chose a two-step procedure: (1) A similarity score for each pair of synonymous words from the same dataset is inferred using supervised learning, and (2) these inferred similarities are used as input for unsupervised clustering.


in a 5-fold approach we only have 80 samples to train and 20 to test.


91 % of the remaining novels have a t-distributed 95 % confidence interval ă 0.5; e.g., given a mean of 3, the confidence interval typically ranges from 2.75 to 3.25.

for our purposes the ratings form a reliable consensus.


In this article, we assume that trees are binary, that each node X is annotated with a head h (notation: X[h]), and that the only unary nodes are parents to a terminal.

we only need unary reductions immediately after a SHIFT.


its possible ridge-regression over all the features at once could give us the same result (i.e.


We primarily focused on Twitter data that was a couple years removed from the last census, which may explain the improvement.

we also ran an experiment using the Twitter data from (Schwartz et al., 2013) which spans 2009 to 2010, and found similar results: the residualized control approach improved the Pearson r for 'increased price' from 0.36 to 0.44 and for 'foreclosure' from 0.65 to 0.69.


Because the ROUGE optimisation problem is NP-hard, one may suspect that exchanging a greedy strategy out for an exact global approach would lead to substantial improvements in system performance.

for our three datasets, we generate gold extractive summaries using both exact and greedy global oracle approaches.


These summaries can be incoherent or incomprehensible due to unresolved pronouns and references, and sentences containing irrelevant information (Nenkova and McKeown, 2011), and this is particularly problematic with informal web text.

abstractive summarization is critical for the web, with rewriting of extracted sentences, as humans write summaries (Jing and McKeown, 1999).


However, for LAMBADA, correct answers can range over the entirety of V, making the output embedding function difficult to train.

we also experiment with a modified version of the Stanford Reader that uses the same embedding function e for both input and output words: where W is an additional parameter matrix used to match dimensions and model any additional needed transformation.


2 No existing dataset treats entities and concepts on a par, which would enable a contrastive analysis of instances and concepts.

we create the data for our study, building two comparable datasets around the binary semantic relations of instantiation and hypernymy (see Table 2).


Exceedance of channel capacity is to be avoided and penalized with additional processing load.

speakers tend to distribute information uniformly across an utterance at a transmission rate close to channel capacity.


He shows that the insertion of a complementizer systematically reduces the surprisal on the following word(s).

if the occurrence of a complement clause is highly unpredictable, the insertion of a complementizer might lead to a more uniform surprisal profile by significantly reducing the high surprisal of the word(s) to follow.


The distinction between those cases and "genuine" cases of article omission thus requires a corpus, in which the relevant cases are explicitly annotated.

we tested our hypothesis on the FraC corpus (Horch 2016), which is text type-balanced and has been annotated by hand for different types of ellipses.


In other words, we do not know at which epoch our algorithm should stop.

lSSVM holds an important advantage over online methods.


In LSP E , the candidate graph, by construction, does not contain cycles, and the inference by Edmonds' algorithm is reduced to selecting for each node an incoming edge with a maximum weight, in other words, the best antecedent or no antecedent for each mention.

the difference between our LSP E and cort is only due to a different implementation.


Our higher-order skip-gram (HOSG) replaces this Gaussian with a multinomial.

hOSG attempts to maximize the log-likelihood Note that as before, we are taking the total count N jk = i X ijk to be observed.


If a command comes between 11pm and 6am, we mark it with 0, assuming that only adults can be awake during these hours.

two feature datasets were created: 1) time usage data that contains usage frequency per hour and weekday as well as content type, 2) ratio usage data that includes distribution of kids and non-kids content requested from a given device.


We propose a simple method that extracts audio samples from movies using textual sentiment analysis.

it is possible to automatically construct a larger dataset of audio samples with positive, negative emotional and neutral speech.


The off-line trained policy is not guaranteed to work well in real world scenarios.

online policy learning has been of great interest.


From those 14192 synsets, 14166 were pretagged with the Music domain (99.8%).

the remaining 26 synsets and all the rest containing the album label were tagged or re-tagged with the Music domain.


All children nodes of a connected node are connected by their most immediate parent.

all children nodes of the mapped YAGO nodes are effectively connected to LKIF through this mapping.


As explained in the previous Section, we applied the paired ttest in five different samples of the corpus to assess whether the difference between classifiers was significant or not, and we found that two out of five of the obtained results were not significantly different (p < 0.05), but the other three were.

it seems that Curriculum Learning, at least the way we applied it here, does not introduce an improvement.


Moreover, the YAGO and NERC levels seem to be coherent with each other, while the LKIF level seems disconnected from the other two.

it seems that the chosen level of granularity for legal NERC using our ontology should be either the 6-class level or the YAGO level, depending on the level of granularity that is required.


The very short ABS task in DUC-2003 and2004 (Over et al., 2007) is a typical example because it requires the generation of a summary in a pre-defined limited output space, such as ten words or 75 bytes.

the repeated output consumes precious limited output space.


denotes element-wise product.â is calculated by: where Sigmoid(•) and ReLu(•) represent the element-wise sigmoid and ReLU (Glorot et al., 2011), respectively.

,r We incorporate two separated components,r andĝ, to improve the frequency fitting.


The purpose ofĝ is to distinguish whether the target words occur or not, regardless of their frequency.

g can be interpreted as a gate function that resembles estimating the fertility in the coverage (Tu et al., 2016) and a switch probability in the copy mechanism (Gulcehre et al., 2016).


Clearly, EncDec+WFE significantly outperformed the strong EncDec baseline by a wide margin on the ROUGE scores.

we conclude that the WFE sub-model has a positive impact to gain the ABS performance since performance gains were derived only by the effect of incorporating our WFE sub-model.


2 A clear disadvantage of this method is that the monolingual parser may not accurately parse those fragments of a sentence which belong to a language unknown to the model.

we consider this as the baseline method.


As a byproduct of this task, we can obtain labelled dependency trees instead.

we also evaluate the output of our parser against the dependency corpora using the evaluator provided with the shared task.


Having non-neutral sentiment for both words is not very common for conj relation (2.3% of the cases), but it much less common for the other relations.

we add features that indicate the sentiment (positive, negative or neutral) for each of the coordinated words.


Regardless of the predictability of automatic metric scores when evaluated with MAE, they unfortunately do not provide a suitable gold standard, simply because they are known to provide an insufficient substitute for human assessment, often unfairly penalizing translations that happen to be superficially dissimilar to reference translations (Callison-Burch et al., 2006).

for WMT-16, the gold standard was modified to take the form of a linear combination of two human-targeted translation edit rate (HTER) (Snover et al., 2006) scores assigned to a given document.


However, minimum Bayes-risk (MBR) decoding is also a very powerful framework for combining diverse systems (Sim et al., 2007;de Gispert et al., 2009).

we study combining traditional SMT and NMT in a hybrid decoding scheme based on MBR.


• NMT decoding is computationally expensive.

it is difficult to collect the statistics needed for risk calculation for NMT.


(2016), we argue that good crosslingual word representations should preserve both monolingual and cross-lingual representation quality.

similar to (Duong et al., 2016;Upadhyay et al., 2016), we test cross-lingual WEs in two core semantic tasks: monolingual word similarity (WS) and bilingual lexicon induction (BLI).


Hence the corresponding hyperparameters in DV-LSTM are all set to 0 unless stated explicitly.

dV-LSTM is expected to have a disadvantage in the tuning of hyperparameters.


On the other hand, the Brown Corpus has a relatively strong overlapping between topics and genres.

features such as TF-IDF or PV-DM that have good estimates of the term frequencies of topic related words/phrases could perform better.


For example, with this method, the embeddings for the bigrams shark killer and killer shark would be the same.


In this paper, we argue that while we incorporate the prior knowledge with contextbased embeddings, words with different occurrences should be treated differently.

we propose to rely on the measurement of information content to control the degree of applying prior knowledge into context-based embeddings-different words would have different learning rates when adjusting their embeddings.


): In addition to the word's frequency, in fact, we believe that the contextual diversity plays a critical role of affecting the quality of word embedding.

we propose a score function based on the conditional entropy (information content) from the information theory.


pointed out that the metaphorical uses of words constitute a large portion of the dependency features extracted for abstract concepts from corpora.

distributional clustering of abstract nouns with such features identifies groups of diverse concepts metaphorically associated with the same source domain.


If we compose the meaning representations for red and herring, we might expect to get a very different representation from the one which could be directly inferred from corpus observations of the phrase red herring.

any judgements of the similarity of two composed phrases may be confounded by the degree to which those phrases are compositional.


On the other hand, the observed cooccurrences of non-compositional target phrases are much less likely to have occurred with either of the constituents independently.

a good compositionality function, without any access to the observed co-occurrences of the target phrases, is highly likely to return vectors which are similar to observed phrasal vectors for compositional phrases but much less likely to return similar vectors for non-compositional phrases.


In order to examine the contribution of both the aligned and unaligned APTs in the composition process, we used a hybrid method where the composed representation is defined as:   In the case where representations consist of APT weights which are probabilities, PPMI is estimated after composition.

we refer to this as compose-first (CF) in contrast to composesecond (CS) where composition is carried out after PPMI calculations.


However, a small but statistically significant performance gain is generally made using the hybrid model.

aligned APT composition and unaligned APT composition are predicting different contexts for compound nouns which all contribute to a better estimate of the compositionality of the phrase.


We attribute this to the fact that some synonyms z are close to the original hyponym x, while others can be distant.

neighbor regularization is able to safeguard the model during training from more errors.


We note that SCL-MI was designed for single-source cross-domain classification .

the use of multiple training domains may be the reason of its lower, but still competitive, performance.


In a similar vein to the writer-reader distinction, identifying the holder or source of an opinion or sentiment also aims at describing the affective information entailed in a sentence in more detail (Wiebe et al., 2005;Seki et al., 2009).

opinion statements that can directly be attributed to the writer can be distinguished from references to other's opinions.


However, a too narrow rating distribution (i.e., most of the ratings being rather neutral relative to the three VAD dimensions) may be a disadvantageous property for training data.

we regard the emotionality of ratings as another quality criterion for emotion annotation complementary to IAA.


For the four categories with a reasonable IAA, Joy, Anger, Sadness and Fear, our best models, on average, actually outperform human agreement.

our data shows that automatically mapping between representation formats is feasible at a performance level on par with or even surpassing human annotation capability.


As the use of social media grows, more users are sharing interests or experiences with products, and asking friends for information (Morris et al., 2010).

social media posts can contain information useful for marketing and customer relationship management, including user behavior, opinions, and purchase interest.


c a i = * means that a basic rule is applied without considering context to make sure that at least one translation is produced for any inputs during decoding.

basic rules are the same as rules in the conventional graph-based model.


Although we reduce rules to phrase pairs to train the reordering model, LR-Hiero decoder uses SCFG rules for translation and the order of source phrases (spans) are determined by the non-terminals in SCFG rules.

we cannot simply rely on the previously translated phrase to compute the orientation and reordering scores.


In Figure 3, rules #5,#4 are monotone, while both are covered by the current span in S. Since the stack always contains one span, this algorithm runs in O(1).

only a limited number of comparisons is used to update S and compute orientation.


Images can bring useful complementary information to MT (Calixto et al., 2012;Hitschler et al., 2016;Huang et al., 2016).

we explore the potential of multi-modal, multilingual MT of auction listings' titles and product images from English into German.


The language model is quite reliable at correcting non-referential uses of it, if the pronoun was part of a phrase that usually contains a neuter form.

we trust Moses over the co-reference annotation in these cases.


We hypothesize that reviews of each subcategory (e.g., gaming laptops) all contribute to the topics of its corresponding general category (e.g., laptops), but with different weights.

aspects of a specific sub-category of products will be the combination of its unique aspects and the aspects from its parent (and thus shared with its siblings).


While this repository of human knowledge abounds with useful information that may unlock new, promising research directions or provide conclusive evidence about phenomena, it has become increasingly difficult to take advantage of all available information due to its sheer amount.

a technology that can assist a user to quickly locate the information of interest is highly desired, as it may reduce the time required to locate relevant information.


However, a significant portion of published paper abstracts is unstructured, which makes it more difficult to quickly access the information of interest.

classifying each sentence of an abstract to an appropriate heading can significantly reduce time to locate the desired information.


The data for Task C presents a higher number of negative than positive examples.

we automatically extended the set of positive examples in our joint MTL training set using the data from Task A.


Finally, we do not report the results of the auxiliary tasks for lack of space and also because our idea of using MTL is to improve the target Task C. Indeed, by their definition, tasks A and B are simpler than C, and are designed for solving it.

attempting to improve the simpler A and B tasks by solving the more complex Task C, although interesting, looks less realistic.


The encoder-decoder performs classification by generating paths in the hierarchy from top node to leaf nodes.

we model the structure of the hierarchy more directly than prior work.


However, under such a parameterization, exact inference and decoding are intractable.

we derive a sampling-based decoding algorithm.


BASIS and CONTRAST sentences have a less prototypical syntactic realization, and they also occur at less predictable places in the document.

it is far more difficult for both machine and human to recognize such sentences.


It is supposed that the farther the positive and negative examples are separated by the discrimination function, the more accurately we could separate unseen test examples with high generalization performance.

let us consider two hyperplanes called separating hyperplanes: (2) (3) can be written in one formula as: Distance from the separating hyperplane to the point xi can be written as: the margin between two separating hyperplanes can be written as: w,b;xi) xi ;Yi = l xi;yi=--i  To maximize this margin, we should minimize Hwll.


Table 4 shows the relationship between the dimension of the kernel function and the parsing accuracy under the condition k --5.

the case of d ----4 gives the best accuracy.


Because supervised training typically demands significant human involvement (e.g., annotating the parse trees of sentences by hand), building a new corpus is a labor-intensive task.

it is worthwhile to consider ways of minimizing the size of the corpus to reduce the effort spent by annotators.


For our sample selection experiment, we chose to work under the more stringent condition of partially supervised training data, as described above, because our ultimate goal is to minimize the amount of annotation done by humans in terms of both the number of sentences and the number of brackets within the sentences.

the quality of our induced grammars should not be compared to those extracted from a fully annotated training corpus.


We also plan to focus on the constituent units within a sentence as training examples.

the evaluation functions could estimate the training utilities of constituent units rather than full sentences.


Third, unlike in CFGs, the parse trees (also known as derived trees in the LTAG) and the derivation trees (which describe how elementary trees are combined to form parse trees) are different in the LTAG formalism in the sense that a parse tree can be produced by several distinct derivation trees.

to provide training data for statistical LTAG parsers, an LTAG extraction algorithm should also build derivation trees.


Second, some non-adjacent pieces in a partition can be glued together to form a bigger piece.

each partition will result in one or more decompositions of the ttree.


(T2) Coordination in XTAG: the templates for coordinations in XTAG are generated on the fly while parsing (Sarkar and Joshi, 1996), and are not part of the 1004 templates.

the conj-etrees in G2, which account for 3.4% of the template tokens in the Treebank, do not match any templates in XTAG.


For example, the two grammars treat reduced relative clauses differently.

the templates used to handle those phenomena in these two gram-mars do not match according to our definition.


Nevertheless, there are several differences: only LexTract explicitly creates fully bracketed ttrees, which are identical to the derived trees for the sentences.

building etrees can be seen as a task of decomposing the fully bracketed ttrees.


For instance, in (2), we find the exact same sequence of parts of speech, but (2a) is a particle use of on, while (2b) is a prepositional use.

the accuracy on the rarer RP (particles) category is as low as 41.5% for the Baseline model (cf.


We use sections 00-19 as the training data and 20-24 as test data.

the performance is on large scale task instead of small scale task on CoNLL-2000 with the same evaluation program.


An example of context co-occurrence probabilities is shown in Figure 3  Co-occurrence Probability Context co-occurrence probabilities can ham dle long-distance lexical dependencies while a standard trigram model can handle local contexts more clearly: in this way they complement each other.

language modeling of their linear interpolation is employed.


In word segmentation, when there are no entries in the dictionary that can match, the input is segmented into single characters.

lin et al (1993) noted that single-character sequences after word segmentation might indicate segmentation problems.


Similar to matched words in the maximum likelihood path, the error detection performance of single-character sequences may depend on their length.

we plotted the recall and precision of detecting errors against the length of the single-character sequences.


In addition, there may be some improvement in detection performance if features are combined for detection.

we adopt a pattern recognition approach to detect errors.


In TREC,-8, the answer to a question can be from any of the hundreds of thousands of documents in the database, whereas for the reading comprehension task, the answer only comes from the short story associated with the question.

while the TREC-8 QA task requires efficient indexing and retrieval techniques to narrow down to the documents that contain the answers, this step is largely not needed for the reading comprehension task.


While there was an overall improvement in recall using the new approach, its performance varied signiGcantly from dom~;~ to domain.

the recall did not always improve dramatically by using a larger beam width.


In Examples 4 and 5, knowing that the complement of expect is an NP or an SBAR provides information as to whether the sense is "await" or "require".

examples 3-5 illustrate how the syntactic context of a word can help determine its meaning.


It does not make any unwarranted assumptions, which means that maximum entropy learns exactly what the data says.


Given a feature set .T, let C be the subset of all distributions P that satisfies the requirement P(fi) = P(fi): We choose a probability distribution consistent with all the facts, but otherwise as uniform as possible.

the uniformity of the probability distribution p(ylx) is measured by the conditional entropy: the probability distribution with maximum entropy is the most uniform distribution.


It is included to reflect the influence of the position of a word on being a segmentation position.

the lexical context of a word is represented by 17 attributes as shown in Figure 2.


A symbol '#' represents a segmentation position marked by human annotators.

the lexical context of word when includes the value 1 for attribute s_position?


The method seems to work quite well since no important degradation is observed in performance for values of p greater or equal to 5% (this may indicate that there are many irrelevant or highly dependant attributes in the WSD domain).

this modification significantly increases the efficiency of the learning process (empirically, up to 7 times faster) with no loss in accuracy.


ed~/ The D50 corpus contains sentences from two different corpora, namely Wall Street Journal (WSJ) and Brown Corpus (BC).

it is easy to perform experiments about the portability of alternative systems by training them on the WSJ part and testing them on the BE part, or vice-versa.


Therefore, local context attributes have to be binarized in a preprocess, while the topical context attributes remain as binary tests about the presence/absence of a concrete word in the sentence.

the number of attributes is expanded to several thousands (from 1,764 to 9,900 depending on the particular word).


The binary representation of attributes is not appropriate for NB and EB algorithms.

the 15 local-context attributes are taken straightforwardly.


The variety of sizes and distribution among the types of NEs shows that using search engines with different capabilities yields different figures for the collections of pages.

the subsequent process of NE acquisition heavily depends on the means used to collect the basic textual data from which knowledge is acquired.


a search for specific relations between nodes in the annotation such as dominance or linear precedence.

many queries a linguist would like to ask using a syntactically annotated corpus either cannnot be expressed in a regular expression based language or at least cannot be expressed in an intuitive way.


Such modifications would affect the complexity of the language and the performance of the tool.

the decision was taken to keep the language as simple as possible in the beginning.


2 does not have a unique root node, and the two nodes with label SINPX have neither a dominance nor a linear precedence relation.

(3) wen glaubst du liebt Maria whom believe you loves Maria 'whom do you believe Maria loves' the models of our queries are defined as more general structures than finite trees.


considering only the database, the dominance and linear precedence relations are not necessarily trA.n~itive.

the query tool can be easily adapted to other data structures, for example to feature structures with reentrancy as annotations.


This is an indication, that for small values of the parameter k the additional two features, i.e., the members' confidence , do not enhance but distort the representation of instances.

the close neighborhood of the unclassified instance is not a legitimate, but a spam e-mail.


By taking advantage of lexical ontologies, we achieve strong results on noun compounds for which neither word is present in the training set.

we think this is a promising approach for a variety of semantic labeling tasks.


It is dicult to compare his method with ours directly because he used a dierent corpus (the EDR corpus), but the part-of-speech categories and the definition of morphemes he used were similar to ours.

this comparison is helpful in evaluating our method.


However, our model learns whether or not a given string is a morpheme and has a huge amount of data for learning what in a corpus is not a morpheme.

we believe that the characteristics of our model led to its good results for identifying unknown words.


Their accuracy of 95% when using the Kyoto University corpus is similar to ours, but they added to their dictionary all of the words appearing in the training corpus.

their experiment had to deal with fewer unknown words than ours did.


We saw that MWU headword evaluations using WordNet provide similar results to those obtained from far more extensive webbased resources.

one could safely use WordNet as a gold standard for future evaluations.


For long abbreviations (five or more characters), the definition should not be longer than the abbreviation length plus 5.

the maximum length of a definition D of an abbreviation A is calculated as follows.


Furthermore, the abbreviation matcher consists of 5 simple match routines and each routine is dedicated to a certain type of abbreviations.


On the other hand, maximizing CL will push the prior for sense 1 arbitrarily close to zero.

its conditional predictions become more accurate at the cost of its joint prediction.


On the other hand, if no trigger words occur in that instance, there will be no other explanation for s other than the presence of w and the other non-indicative words.

w's weight, and the other words', will grow until s is predicted sufficiently strongly.


The model as drawn assumes that each observation is independent, but we could add any arrows we please among the o i without changing the conditional predictions.

it is common to think about this model as if the joint interpretation were absent, and not to model the observations at all.


But more subtly note that states are independent of future observations.

future cues are unable to influence past decisions in certain cases.


sense id's such as church/BUILDING and church/INSTITUTION or commonly confused spellings such as quiet and quite), and are dependent on local and long-distance collocational and syntactic patterns to resolve between the set of alternatives.

both tasks can share a common feature space, data representation and algorithm infrastructure.


They assume that the easiest types of coreference relationships to resolve are those that occur with high frequencies in the data.

they mine by hand three sets of coreference rules for covering positive instances from the training data by finding the coreference knowledge satisfied by the largest number of anaphor-antecedent pairs.


has been identified for the anaphor NP¨£ .

all (positive and negative) training instances formed with NP¨£ as the anaphor are no longer needed and are subsequently removed from the training data.


12 Although it might be beneficial to acquire these rules at the classification level (according to the learning algorithm), they can be detrimental to system performance at the clustering level, especially if the rules cover a large number of examples with a lot of exceptions.

it is necessary to know which rules are worthy of keeping at the clustering level and not the classification level.


In addition, our experiments indicate that the positive sample selection algorithm does not guarantee that hard instances can be entirely excluded.

we proposed an error-driven rule pruning algorithm that can effectively enhance the precision of the system by dis-carding rules that cause the ruleset to perform poorly with respect to the coreference scoring function.


Or, having arrived at S fund → To fund NP, it might transform it into S fund → To fund PP NP and then further to S fund → To fund NP PP before halting.

p θ (S fund → To fund NP PP) denotes the probability that the random walk somehow reaches S fund → To fund NP PP and halts there.


The alignment probabilities for word # " depend only on the alignment of the previous word # " # $ § if using a first order HMM.

source words are not awarded/penalized for being aligned to more than one target word.


In this paper we only address the translation model and assume that there exists a one-to-one alignment from target to source words.

without loss of generality is: Here each © " gives the index of the word is aligned.


National reserve and Canadian parliament, are examples of such pairs.

there will be an indirect association between the English noun and the translation of the English adjective.


Even constituents annotated flat in the Treebank are first given a more hierarchical, recursive structure.

the trees generated during parsing will be richer than those in the Treebank.


Clustering n items into k classes corresponds to the choice of an ordering for the labels on the axes of the Gram matrix and the choice of k − 1 change points marking the boundaries between the blocks.

the search space of clusters is very large.


While these structures often do capture semantic siblings, they frequently capture other types of semantic relationships as well.

we use heuristics to isolate subsets of these syntactic structures that consistently contain semantic siblings.


Probably the main reason, however, is that there are many more proper names associated with most semantic categories than there are general nouns.

we evaluated the PNP and GN lexicons differently.


However, none of these approaches learn simultaneously the translation of phrases/templates and the translation of words.

there is a chance that the learning procedure will not discover phrase-level patterns that occur often in the data.


In addition to the exact calculation, we also experimented with establishing exact probabilities via p(w|h, s) only at the end of each token in x, and assuming that the probabilities of the intervening characters vary linearly between these points.

of this assumption, 1 |h, s) is constant for all k between the end of one word and the next, and therefore can be factored out of the sum in equation 2between these points.


For example, DropVerb may be a higher priority rule for buy questions than for support questions, since the verbs indicative of buy questions (typically "buy" or "sell") are often absent in the target product pages.

we investigated a machine learning approach to automatically obtain the optimal rule application sequence.


However, since these hub-pages are typically not content-rich, they often do not receive high scores from the search engine (over which we have no control).

we developed a mechanism to explicitly retrieve these hubpages when appropriate, and to combine its results with the outcome of the actual search process.


Furthermore, most three and four-word queries contain two distinct noun phrases, such as "visualage for java" and "application framework for e-business".

we use the 2NP baseline as an approximation of user keyword search performance for our natural language questions.


However, these cases focused on improving text retrieval methods and did not address problems in improving speech recognition.

errors in recognizing spoken queries (error rate was approximately 30%) considerably decreased the retrieval accuracy.


Even if speech recognition is not perfect, these documents are potentially associated with the user speech more than the entire col-lection.

we search only these documents for index terms corresponding to detected OOV words.


In this case, it is possible that retrieved documents contain the word "kankitsu (citrus)."

we replace the syllable sequence /ka N ke tsu/ in the query with "kankitsu," which is additionally used as a query term in the second stage.


In other words, OOV words were modeled as sequences of syllables.

by using our language model, OOV words can easily be detected.


For example, /ka N ki tsu/ has to be retrieved efficiently in response to /ka N ke tsu/.

we implemented a forward/backward partial-matching method, in which entries can be retrieved by any substrings from the first/last characters.


In cases of methods 2-4, queries dictated by four speakers were used independently.

in practice we compared 13 different retrieval results.


In the final case, a fragment of a narrative sentence consisting of ten words was detected as a single OOV word.

our method, which can complete up to two word sequences, mistakenly processed that word, and decreased the retrieval accuracy.


Through our experiments, we found that P(H i |w i-2 w i-1 ) is a poor estimator of category probability; in fact, the unigram estimate P(H i ) achieved better results in our experiments as shown in Section 6.1.

we also used the unigram estimate for word category probability in our 3 Since the POS-tagger does not identify phrases, our implementation does not identify precisely one headword for a phrase, but identify multiple headwords in the case of compounds.


In this study, we performed word clustering for words and headwords separately.

we have the following two predictive clustering models, (8) for words and (9) for headwords: Substituting Equations (8) and 9into Equation 7, we get the cluster-based PHTM of Equation 10, referred to as C-PHTM.


In PCTM, the probability for all words is estimated by In Table 2, we find that for both PHTM and HTM, models U-HTM and U-PHTM achieve better performance than models T-HTM and T-PHTM.

only models using unigram for category probability estimation are used for further experiments, including the models with predictive clustering.


System Architecture answer redundancy in the source data collection, the more likely it is that we can find an answer that occurs in a simple relation to the question.

the less likely it is that we will need to solve the aforementioned difficulties facing natural language processing systems.


We do not count frequency of occurrence within a summary (the usual tf component in ranking schemes).

the final score for an n-gram is based on the weights associated with the rewrite rules that generated it and the number of unique summaries in which it occurred.


Typically, when deploying a question answering system, there is some cost associated with returning incorrect answers to a user.

it is important that a QA system has some idea as to how likely an answer is to be correct, so it can choose not to answer rather than answer incorrectly.


Recall that in cases of annotator disagreement, the P alignment is taken to be the union of the P alignments of both annotators.

it is possible for the P alignment to contain two mutually conflicting alignments.


One possibility is that this is due to most phrases having a head and modifier pair to test, while many do not have multiple modifiers and therefore there are fewer opportunities for modifier crossings.

it is informative to examine how many potential crossings actually turn out to be crossings.


In most of the cases of rewording (see Figure 4) or re-  Figure 5) a more "parallel" translation would also be valid.

while it would be difficult for a statistical model to learn from these examples, there is nothing to preclude production of a valid translation from a system using phrasal movement in the reordering phase.


Our intuitions about the cohesion of syntactic structures follow from the notion that translation, as a meaning-preserving operation, preserves the dependencies between words, and that syntactic structures encode these dependencies.

dependency structures should cohere as well as, or better than, their corresponding syntactic structures.


Surprisingly, against our linguistic intuition the ana npform feature appeared to be the most important one.

we expected considerable differences in the performance of our classifier with respect to the NP form of the anaphor under consideration.


Thus we know the English POS of the word we are trying to generate in addition to the foreign word that is generating it.

we condition translation on English POS as well as the foreign word.


The FSM constructed for a test sentence is subsequently composed with a FSM trigram language model created via the SRI Language Modeling Toolkit (Stolcke, 2002).

we use the trigram language model to implement rescoring of the (direct) translation probabilities for the English word sequences in the translation model lattice.


We choose the threshold that maximizes the likelihood of the first pass output.

the number of English documents £ in (1), as well as the interpolation weight in (2), are chosen dynamically for each Mandarin story to maximize the likelihood of the ASR output.


Furthermore, a documentaligned corpus is much easier to build.

a much bigger and more reliable comparable corpus may be used, and eventually more accurate trigger-pairs will be acquired.


The problem is that there are many sentences that convey the same information but have little surface resemblance.

simple word counts cannot distinguish the matching pair (A) in Figure 1 from the unrelated pair (B).


If, however, the parent has fewer unresolved gaps (p¨ch), the antecedent of the EE is among the children.

the algorithm attempts to find this antecedent (lines 11-18).


Indeed, as the number of EEs increases, so does the number of nonterminals, which results in increasingly severe sparse data problem.

there is a trade-off between finding local phrase structure and long-distance dependencies.


Nonetheless, the PRO&WH model inserting EEs can match neither the accuracy for antecedent recovery nor the time efficiency of the pre-processing approach.

the results show that treating EE-detection as a preprocessing step is beneficial to both to antecedent recovery accuracy and to parsing efficiency.


In particular, a local window of five words, accompanied by the gap+ threads proved to be crucial.

we claim that, in order to detect long-distance dependencies, a robust stochastic parser should integrate lexical information as well as local cues cutting across phrase boundaries by either incorporating them into the probability model or using them in the beam-search.


In particular, we only include as semantic roles those instances in the propbank such that in the extracted TAG they are localized in the same elementary tree.

adjunct semantic roles (ARGM's) are basically absent from our test corpus.


Also, these features represent syntactic categories about which there is a broad consensus in the literature.

we believe that our results are portable to other frameworks and differently annotated corpora such as dependency corpora.


ME models are log-linear models in which feature functions map specific instances of syntactic features and classes to binary values (e.g., if a training element has head="in" and role=CAUSE, then, for that element, the feature function f(CAUSE, "in") will equal 1).

mE is not here being used as another way to find weights for an interpolated model.


Recall is calculated based on the total number of frame elements in the test set, not on the total number of elements that have matching parse constituents.


"might not have gone"), the CCG parser assumes that the first auxiliary (might) is head, whereas PropBank assumes it is the main verb (gone).

ccG assumes that not modifies might, whereas PropBank assumes it modifies gone.


When read in context, each sentence would appear to contain roughly the same amount of information, per word, whether it is the first sentence or the tenth one.

the tenth sentence, when taken out of context, must appear significantly more informative (and therefore harder to process), since it implicitly assumes that the reader already knows all the information in the preceding nine sentences.


That is, for each unit feature we capture the relative corpus frequency, frequency relative to the target word frequency, and frequency relative to other features in the same feature cluster.

for an nvalued one-dimensional feature cluster, we generate 3n independent feature values.


For each parse p, its score Sco(p) is defined as follows: where M T (p) is the output of the tree kernel SVMs, M L (p) is the output of linear kernel SVMs, l(p) is the log-likelihood of parse p, and b(p) is the number of brackets in parse p. We noticed that the SVM systems prefers to give higher scores to the parses with less brackets.

the system has a high precision but a low recall.


As a result, the system has a high precision but a low recall.

we take the number of brackets, b(p), as a feature to make the recall and precision balanced.


In addition, many subjective terms occur infrequently, such as strongly subjective adjectives (e.g., preposterous, unseemly) and metaphorical or idiomatic phrases (e.g., dealt a blow, swept off one's feet).

we believe that subjectivity learning systems must be trained on extremely large text collections before they will acquire a subjective vocabulary that is truly broad and comprehensive in scope.


However, incorporating instances that are confidently labeled by both classifiers may reduce the probability of acquiring new information from the data.

the method imposes an additional preference for instances that are confidently labeled by one but not both (Preference 2).


In contrast, our ranking method is more conservative in that it places more emphasis on maintaining labeled data accuracy than the B&M method.

the classifier learns at a slower rate when compared to that in the B&M case: it is not until iteration 600 that we see a sharp rise in F-measure.


The features with a zero parameter can be removed from the model without changing its prediction behavior.

we can consider that the inequality ME model embeds feature selection in its estimation.


We also observed that the LMVM method converges very quickly for the text categorization datasets with an improvement in accuracy.

we use the LMVM method (and its variant for the inequality models) throughout the experiments.


Although the Gaussian MAP estimation can use different σ i for each feature, we used a common variance σ for gaussian.

gaussian roughly corresponds to single in the way of dealing with the unreliability of features.


In this method, the optimization is done in a parallel fashion by updating all of the parameters at the same time.

the resulting classifier uses all the features that are included in the model and lacks sparseness.


As we have seen in the previous sections, the ability to incorporate more features has a big impact on the accuracy.

a dual perceptron algorithm may have a large advantage over other methods.


This is particularly not appropriate for the prepositional phrase because the preposition is always the head word of the phrase in the UPenn Treebank annotation.

we also added the opposite child of the first exposed previous head into the context for predicting.


However, reduction in PPL for a language model does not always mean improvement in performance of a real application such as speech recognition.

future study on applying the neural network enhenced SLM to real applications needs to be carried out.


Presumably this is because if it can be said of a "chairperson" that she can "preside at meetings" or that "a remark" can be "addressed to her", then logically speaking these things can be said of the superordinates of "chairperson", like "person", as well.

information at the synset level is relevant also at the supersense level.


This might even be a quite frequent sense for a specific corpus, e.g., the word "issue" in the sense of "shares, stocks" in the WSJ.

there is a strong inter-action between word sense disambiguation and reference resolution (see also (Preiss, 2002)).


The instantiation universities and other schools, e.g., does not occur at all in the British National Corpus (BNC), a 100M words corpus of British English.

11 we use the largest corpus available, the Web.


This is a consequence of the substitution of the antecedent ("Will Quinlan") with its NE category ("person"); such an instantiation is not frequent, since it violates standard relations within (O1).

we also instantiate (O1) by substituting N ½ with Ò , and N ¾ with the NE type of the antecedent (Á Ô ¾ in Table 3).


Since we have to take various factors into consideration, it is difficult to find a good combination of heuristic rules.

the machine learning approach is attractive, but it requires a large amount of training data.


We have to take even more factors into account, but it is difficult to maintain such heuristic rules.

recent studies employ machine learning approaches.


It takes a long time to construct high-quality annotated data, and we want to compare our results with conventional methods.

we obtained Seki's data (Seki et al., 2002a;Seki et al., 2002b), which are based on the Kyoto University Corpus 2 2.0.


The second step excludes verb phrases, adjective phrases, and clauses.


A verb usually does not have the same entity in two or more cases (Murata and Nagao, 1997).

our resolver excludes candidates that are filled in other cases of the verb.


We put Ag before Sa because Kameyama's method was better than Walker's in Okumura and Tamura (1996).


Although we can consider various other features for zero pronoun resolution, it is difficult to combine these features consistently.


When we eliminated these three features, vrads+svm2's score for editorial dropped by 4 points.

combinations of these three features are useful.


One word may belong to two or more semantic categories, and each semantic category has superclasses (e.g., 'father' has the superclass 'parent').

we keep all of these categories and their superclasses in a category list for the candidate.


The first and second patterns' subjects are restricted to AGENT, and the third pattern's subject is restricted to PEOPLE.

the subject of yomu is accepted if and only if it is AGENT or PEOPLE.


The character-level tags of the constituent " " 6 # 6 % " are the lower-cased word-level POS tag plus a positional letter.

the first character " " " is assigned the tag "nrb" where "nr" is from the word-level POS tag and "b" denotes the beginning character; the second (middle) character "# " gets the positional letter "m", signifying that it is in the middle, and the last character "% " gets the positional letter "e", denoting the end of the word.


It is clear that character-level tags encode word boundary information, and chunk-level 1 labels are word-level POS tags.

parsing a Chinese character sentence is effectively doing wordsegmentation, POS-tagging and constructing syntactic structure at the same time.


During training, the NE boundaries were provided to the word segmenter; the latter is restricted to enforce word boundaries at each entity boundary.

at training time, the word boundaries are consistent with the entity boundaries.


Only a limited set of characters are used as family names, while the first name can be any character(s).

the family name is a very important and useful feature in identifying an NE in the person category.


Note that parts for a basic PCFG model are not just rewrites (which can occur multiple times), but rather anchored items.

formally, we assume some countable set of parts, R. We also assume a function R which maps each object (x, y) ∈ X × Y to a finite subset of R. R(x, y) is the set of parts belonging to a particular parse.


Since AL typically selects more ambiguous examples, a weak model has more difficulty getting the best parse within the n-best when AL is used.

the gains from the more informative examples selected by AL are surpassed by the gains that come with the easier labeling with random sampling.


A deterministic parser returns a single parse for a given input, while a stochastic parser may return more than one.

the problem of inducing a stochastic parsing automaton consists in specifying the automaton's data structures, its dynamics (what the actions do), and then learning a stochastic policy.


Matching of single anchors relies on the general distributional similarity principle and unlike the other methods does not require redundancy of sets of multiple anchors.

a much larger number of paraphrases can be found in a regular corpus.


TE then substitutes each slot anchor (see section 3.1) in the parse graphs with its corresponding slot variable.


Because we are using small training sets, parameter estimates for a lexicalized Korean probabilistic grammar are likely to be highly unreliable due to sparse data.


Our bilingual parser implementation is on the cusp of practicality (in terms of memory requirements); when the grammar constant increased, we were unable to parse longer sentences.

the results given for bilingual parsing are on reduced test sets, where a length filter was applied: sentences with |E| + |F | > τ were removed, for varying values of τ .


Words and sentence boundaries are mutually constrained via syntactic structure.

the word identities themselves (from automatic recognition or human transcripts) constitute a primary knowledge source for the sentence segmentation task.


Breiman (2001) defines a random forest as "a classifier consisting of a collection of tree structured classifiers {h(x, Θ k ), k=1, ...} where the {Θ k } are independently identically distributed random [training] vectors and each tree casts a unit vote for   Section  # sent # words # preds # args  training  28  651  50  129  development  1.2  28  2.2  5.7  test  1.5  33  2.7  7.0   Table 1: Number of sentences, words, marked predicates, and labeled arguments in thousands the most popular class at input x."

bagging (breiman, 1996) is a form of Random Forest, where each tree is grown based on the selection, with replacement, of N random training examples, where N is the number of total examples in the training set.


For example, the worst case training time for SVMs is quadratic in the number of features and this method reduced the dimensionality to 3% of its original size.

the method has the theoretical potential to reduce training time by up to 100(1-0.03 2 ) = 99.91%.


All other slotssubject, object, and indirect object-each form their own singleton slot class.

by dropping the noun, and by treating all prepositional slots as the same slot.


In our approach, all previously annotated slots are used in the iterative training of the probability model.

even when we report results on argument slots only, adjunct and "bad" slots may have induced errors in their labelling.


The following sentences are examples for the semantic annotation provided in the FrameNet corpus for verbs in the IMPACT frame, which describes a situation in which typically "an Impactor makes sudden, forcible contact with the Impactee, or two Impactors both ... [make]  Note that the frame-specificity of semantic roles in FrameNet has important consequences for semantic role assignment, since there is no direct way to generalise across frames.

the learning for automatic assignment of semantic roles has to proceed frame-wise.


learning the regularities of the relationship between semantic roles and their possible syntactic realisation and applying this knowledge.

our main hypothesis is: The more varied the realisation possibilities of the verbs in a frame, the more difficult it is for the learner to learn the correct linking patterns, and therefore the more error-prone semantic role assignment.


Models for role assignment also rely on these regularities, as can be seen from the kind of features used for this task (see Section 3.2), which are either syntactic or lexical.

current models for automatic role labelling rely on the regularities at the syntax-semantics interface.


However, for our purposes it is more convenient for a good cluster to have a high rating.

we turn the cost function into a "quality" function.


The weights are given by the vector sizesin our case, the frequency of the predicates: The weighting lends more importance to wellattested predicates, limiting the amount of noise introduced by infrequent predicates.

our intuition is that 6 7 4 5 should be a better measure than 4 5 for argument structure uniformity.


A priori, this can only be done within frames, since roles are frame-specific, and there is no unique right mapping between roles.


Since the number of categories per word is very small, the parser has little work to do in combining the categories; the supertagger is effectively an almost-parser (Bangalore and Joshi, 1999).

the parsing model, which is not tuned for questions, is hardly used by the parser.


(2002) results in a large derivation space, which must be searched using the parsing model.

the accuracy of the parser is greatly reduced, as shown in rows 2 and 4.


p L such that p i is a tuple t, x where t corresponds to the last of the (possibly multiple) observations made, and x refers to the state we were coming from when we output this observation (phrase).

we want to find: To do this, as in a traditional HMM, we estimate the ζ table.


There is also a null state for each document position r ,i , so that when jumping out of a null state, we can remember what our previous position in the document was.

figure 2 shows the schematic drawing of the PBHMM constructed for the document "a b".


When transitioning Figure 2: Schematic drawing of the PBHMM (with some transition probabilities) for the document "a b" source target probability Table 3: Jump probability decomposition from p to r i,i , the value a p,r i,i = jump (i).

if we begin at the first word in the document, we incur a transition probability of jump (1).


During the transition a → q, ω is emitted.

the probability for the alignment is: jump (1) rewrite ("cd" "a") jump (2).